Neural retrievers that double BM25 performance on QUEST collapse below 0.02 Recall@100 on the new LIMIT+ benchmark while lexical methods reach 0.96, with all methods degrading as compositional depth increases.
ISBN 979-8-89176-332-6
2 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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Pith papers citing it
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external citations · OpenAlex
years
2026 2verdicts
UNVERDICTED 2representative citing papers
NSFL adapts t-norms and t-conorms to embedding spaces with NS-Delta and SQO to enable logical operations, reporting up to 81% mAP gains in retrieval tasks.
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Reproducing Complex Set-Compositional Information Retrieval
Neural retrievers that double BM25 performance on QUEST collapse below 0.02 Recall@100 on the new LIMIT+ benchmark while lexical methods reach 0.96, with all methods degrading as compositional depth increases.
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NSFL: A Post-Training Neuro-Symbolic Fuzzy Logic Framework for Boolean Operators in Neural Embeddings
NSFL adapts t-norms and t-conorms to embedding spaces with NS-Delta and SQO to enable logical operations, reporting up to 81% mAP gains in retrieval tasks.